Advancements in image sensor technology have lead to a demand for higher resolution capabilities, namely the ability to provide images with larger numbers of pixels. For example, image sensors continue to increase in the number of pixels they are able to capture.
In today's world, surveillance technology is pervasive. Surveillance technology allows the remote monitoring of a location, recording events happening at that location, and providing advanced warning of intruders or unauthorized visitors. However, a drawback in some prior art surveillance technology is that the fields of view of typical surveillance cameras are narrow, aimed at specific regions of interest. As such, cameras of a surveillance system may not be pointed in the right direction at the right time. To address this shortcoming of some surveillance systems, surveillance cameras having a greater resolution and greater field of view, based upon these recent advances in image sensor technology, may be employed.
In particular, modern image sensor technology is employed in Wide Area Persistent Surveillance (WAPS). WAPS is the photographic surveillance of an entire geographic area, rather than isolated regions of interest. WAPS is typically gathered by an unmanned aerial vehicle that uses a camera array to take a high-resolution picture of the entire geographic area. This allows the surveillance of an entire city, for example, rather than individual areas of interest in the city, thereby greatly extending the surveillance coverage.
The WAPS images are typically taken at a frequency of 1 to 10 Hz. For convenience, it may be desirable to transcode a plurality of such WAPS images to form a full motion video. However, a single WAPS image may have a resolution of ten gigapixels, for example, and may consume gigabytes worth of data storage space, which may overwhelm existing image processing techniques. Moreover, due to enhancements in satellite location technology, some WAPS images may be georeferenced. Generally speaking, georeferenced images include imagery data encapsulated with geospatial metadata that correlates the pixel space of the imagery to geospatial coordinate values, e.g., latitude/longitude coordinates.
Moreover, in mobile applications, the use of WAPS imagery may suffer from drawbacks. For example, mobile devices may have limited bandwidth and memory resources. Accordingly, the large detail and size of the WAPS files may make such applications ungainly and resource intensive. Also, the typical mobile device may have a limited screen size, thereby making viewing of WAPS imagery problematic. Further, the limited computational resources of mobile devices may make conversion of WAPS imagery undesirable.